OCI AI: Generative AI Professional — Lesson 1: OCI Generative AI architecture, foundation models, service access, compartments, policies, dedicated AI clusters, and inference endpoints

Learning Objectives

• Break down the roles, prerequisites, records, and decision points involved in OCI Generative AI architecture, foundation models, service access, compartments, policies, dedicated AI clusters, and inference endpoints.
• Complete a controlled activity for OCI Generative AI architecture, foundation models, service access, compartments, policies, dedicated AI clusters, and inference endpoints and capture generative AI architecture map and authenticated endpoint test.
• Challenge the result with a scenario involving an inference request failing because the endpoint, policy, and compartment scope do not align, then justify the corrective action.

Detailed Topics

• OCI Generative AI architecture
• Foundation models
• Service access
• Compartments
• Policies
• Dedicated AI clusters
• And inference endpoints
• Decision ownership, prerequisites, and acceptance evidence for OCI Generative AI architecture, foundation models, service access, compartments, policies, dedicated AI clusters, and inference endpoints
• Relevant access, data, integration, performance, recovery, and support considerations

Instructor Demonstration

The instructor builds a representative scenario for OCI Generative AI architecture, foundation models, service access, compartments, policies, dedicated AI clusters, and inference endpoints, narrating each decision and showing how it contributes to a secure OCI generative AI solution that selects suitable models, grounds responses, evaluates quality, and operates under clear governance. The demonstration records generative AI architecture map and authenticated endpoint test so learners can compare configuration intent with observable behaviour.

Hands-On Exercise

Build or execute OCI Generative AI architecture, foundation models, service access, compartments, policies, dedicated AI clusters, and inference endpoints for a defined business case. Introduce the condition “an inference request failing because the endpoint, policy, and compartment scope do not align,” inspect the resulting evidence, correct the cause without discarding the audit trail, and document the final state in generative AI architecture map and authenticated endpoint test.

1. Record the starting state, assumptions, identities, data, and expected outcome.
2. Execute the planned workflow and collect evidence at each material checkpoint.
3. Test one valid path, one boundary condition, and the stated failure scenario.
4. Document the correction, retest result, rollback option, and follow-up owner.

Scenario Analysis

• Clarify the business trigger, affected roles, required records, and acceptable result for OCI Generative AI architecture, foundation models, service access, compartments, policies, dedicated AI clusters, and inference endpoints.
• Compare at least two implementation or correction choices and identify which evidence would distinguish them.
• Decide how generative AI architecture map and authenticated endpoint test should be reviewed, approved, retained, and handed to the next owner.

Troubleshooting Guidance

When results from OCI Generative AI architecture, foundation models, service access, compartments, policies, dedicated AI clusters, and inference endpoints are unexpected, first reproduce the issue with the same role, record, date, and transaction context. Inspect generative AI architecture map and authenticated endpoint test, then test whether “an inference request failing because the endpoint, policy, and compartment scope do not align” explains the evidence before changing configuration. Retest the corrected path and one unaffected path to guard against regression.

Validation Checklist

• The stated requirement for OCI Generative AI architecture, foundation models, service access, compartments, policies, dedicated AI clusters, and inference endpoints is linked to an owner and an observable acceptance condition.
• Generative ai architecture map and authenticated endpoint test contains enough detail for another practitioner to repeat the check.
• Normal processing, one boundary condition, and the identified failure path have all been tested.
• Access, data integrity, downstream impact, rollback, and support ownership are recorded.

Lesson Outcome

Learners can apply OCI Generative AI architecture, foundation models, service access, compartments, policies, dedicated AI clusters, and inference endpoints, explain the decisions made, and defend the result with generative AI architecture map and authenticated endpoint test.